Project Title

Enhanced Screening Methods for the Detection of Mycobacterium tuberculosis complex in Ancient Host Microbiomes

Academic department under which the project should be listed

RCHSS - Geography & Anthropology

Faculty Sponsor Name

Tsai-Tien Tseng

Abstract (300 words maximum)

Diagnosis of tuberculosis (TB) based on skeletal morphology is difficult due to less than 5% of affected individuals developing observable lesions. Diagnosis of TB based on analysis of aDNA is difficult because it belongs to the larger Mycobacterium tuberculosis complex (MTBC), sharing up to ~99% genomic sequence identity with other members. With the advent of next-generation sequencing (NGS), ancient host microbiomes can now be subjected to metagenomic analyses for the detection of tuberculosis. This study aims to develop an enhanced screening method for the detection of MTBC in ancient skeletons to create a more suitable bioinformatics workflow. Our developed workflow was applied onto skeletal samples from 28 individuals representing two Neolithic cultures (SRA number PRJNA422903): the Middle Neolithic Brześć Kujawski Group of the Lengyel culture (∼4400–4000 BC, 26 individuals), and the Late Neolithic Globular Amphora culture ( ∼3100–2900 BC, 2 individuals). Initial quality control steps included adapter trimming with Trim Galore!. Kraken2 was then used for taxonomic classification with a custom-built database that was created specifically to detect MTBC. Various species of Mycobacterium were present in all 28 individuals, with an average of 6% of the Mycobacterium genus sequencing reads mapping to Mycobacterium avium complex (MAC) and an average of 7% mapping to MTBC. This work revealed additional species of MTBC and MAC that were previously unreported by the originator of this dataset, including Mycobacterium tuberculosis XDR1219 and Mycobacterium avium hominissuis. XDR1219 is known to cause extensively drug-resistant TB (XDR-TB). The presence of XDR1219 in this sample can potentially contribute to better understanding towards the lineage and geographic origin of XDR-TB. The aforementioned bioinformatic workflow has therefore been more effective than previously published screening methods and suitable for future paleopathological studies that employ NGS technologies.

Disciplines

Anthropology | Bioinformatics | Biological and Physical Anthropology | Biology | Diseases | Genetics and Genomics | Immunology and Infectious Disease

Project Type

Poster

How will this be presented?

Yes, in person

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Enhanced Screening Methods for the Detection of Mycobacterium tuberculosis complex in Ancient Host Microbiomes

Diagnosis of tuberculosis (TB) based on skeletal morphology is difficult due to less than 5% of affected individuals developing observable lesions. Diagnosis of TB based on analysis of aDNA is difficult because it belongs to the larger Mycobacterium tuberculosis complex (MTBC), sharing up to ~99% genomic sequence identity with other members. With the advent of next-generation sequencing (NGS), ancient host microbiomes can now be subjected to metagenomic analyses for the detection of tuberculosis. This study aims to develop an enhanced screening method for the detection of MTBC in ancient skeletons to create a more suitable bioinformatics workflow. Our developed workflow was applied onto skeletal samples from 28 individuals representing two Neolithic cultures (SRA number PRJNA422903): the Middle Neolithic Brześć Kujawski Group of the Lengyel culture (∼4400–4000 BC, 26 individuals), and the Late Neolithic Globular Amphora culture ( ∼3100–2900 BC, 2 individuals). Initial quality control steps included adapter trimming with Trim Galore!. Kraken2 was then used for taxonomic classification with a custom-built database that was created specifically to detect MTBC. Various species of Mycobacterium were present in all 28 individuals, with an average of 6% of the Mycobacterium genus sequencing reads mapping to Mycobacterium avium complex (MAC) and an average of 7% mapping to MTBC. This work revealed additional species of MTBC and MAC that were previously unreported by the originator of this dataset, including Mycobacterium tuberculosis XDR1219 and Mycobacterium avium hominissuis. XDR1219 is known to cause extensively drug-resistant TB (XDR-TB). The presence of XDR1219 in this sample can potentially contribute to better understanding towards the lineage and geographic origin of XDR-TB. The aforementioned bioinformatic workflow has therefore been more effective than previously published screening methods and suitable for future paleopathological studies that employ NGS technologies.

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